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Unverified Commit 926008bc authored by Chao Liu's avatar Chao Liu Committed by GitHub
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Merge branch 'develop' into batched_gemm_multiD

parents e9652cbc d8415a96
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Showing with 1352 additions and 663 deletions
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View file @ 926008bc
...@@ -11,6 +11,12 @@ def show_node_info() { ...@@ -11,6 +11,12 @@ def show_node_info() {
""" """
} }
def runShell(String command){
def responseCode = sh returnStatus: true, script: "${command} &> tmp.txt"
def output = readFile(file: "tmp.txt")
return (output != "")
}
def cmake_build(Map conf=[:]){ def cmake_build(Map conf=[:]){
def compiler = conf.get("compiler","/opt/rocm/bin/hipcc") def compiler = conf.get("compiler","/opt/rocm/bin/hipcc")
...@@ -60,7 +66,7 @@ def cmake_build(Map conf=[:]){ ...@@ -60,7 +66,7 @@ def cmake_build(Map conf=[:]){
""" """
def setup_cmd = conf.get("setup_cmd", "${cmake_envs} cmake ${setup_args} .. ") def setup_cmd = conf.get("setup_cmd", "${cmake_envs} cmake ${setup_args} .. ")
// reduce parallelism when compiling, clang uses too much memory // reduce parallelism when compiling, clang uses too much memory
def build_cmd = conf.get("build_cmd", "${build_envs} dumb-init make -j\$(( \$(nproc) / 1 )) ${config_targets}") def build_cmd = conf.get("build_cmd", "${build_envs} dumb-init make -j\$(( \$(nproc) / 2 )) ${config_targets}")
def execute_cmd = conf.get("execute_cmd", "") def execute_cmd = conf.get("execute_cmd", "")
def cmd = conf.get("cmd", """ def cmd = conf.get("cmd", """
...@@ -113,7 +119,14 @@ def buildHipClangJob(Map conf=[:]){ ...@@ -113,7 +119,14 @@ def buildHipClangJob(Map conf=[:]){
retimage = docker.build("${image}", dockerArgs + '.') retimage = docker.build("${image}", dockerArgs + '.')
withDockerContainer(image: image, args: dockerOpts) { withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES'){ timeout(time: 5, unit: 'MINUTES'){
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo | tee clinfo.log'
if ( runShell('grep -n "Number of devices:.*. 0" clinfo.log') ){
echo "GPU not found"
throw e
}
else{
echo "GPU is OK"
}
} }
} }
} }
...@@ -125,7 +138,14 @@ def buildHipClangJob(Map conf=[:]){ ...@@ -125,7 +138,14 @@ def buildHipClangJob(Map conf=[:]){
retimage = docker.build("${image}", dockerArgs + " --no-cache .") retimage = docker.build("${image}", dockerArgs + " --no-cache .")
withDockerContainer(image: image, args: dockerOpts) { withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES'){ timeout(time: 5, unit: 'MINUTES'){
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo |tee clinfo.log'
if ( runShell('grep -n "Number of devices:.*. 0" clinfo.log') ){
echo "GPU not found"
throw e
}
else{
echo "GPU is OK"
}
} }
} }
} }
...@@ -133,7 +153,14 @@ def buildHipClangJob(Map conf=[:]){ ...@@ -133,7 +153,14 @@ def buildHipClangJob(Map conf=[:]){
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') { withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 5, unit: 'HOURS') timeout(time: 5, unit: 'HOURS')
{ {
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo | tee clinfo.log'
if ( runShell('grep -n "Number of devices:.*. 0" clinfo.log') ){
echo "GPU not found"
throw e
}
else{
echo "GPU is OK"
}
cmake_build(conf) cmake_build(conf)
} }
} }
...@@ -145,7 +172,6 @@ def reboot(){ ...@@ -145,7 +172,6 @@ def reboot(){
build job: 'reboot-slaves', propagate: false , parameters: [string(name: 'server', value: "${env.NODE_NAME}"),] build job: 'reboot-slaves', propagate: false , parameters: [string(name: 'server', value: "${env.NODE_NAME}"),]
} }
def buildHipClangJobAndReboot(Map conf=[:]){ def buildHipClangJobAndReboot(Map conf=[:]){
try{ try{
buildHipClangJob(conf) buildHipClangJob(conf)
...@@ -162,7 +188,6 @@ def buildHipClangJobAndReboot(Map conf=[:]){ ...@@ -162,7 +188,6 @@ def buildHipClangJobAndReboot(Map conf=[:]){
} }
} }
def runCKProfiler(Map conf=[:]){ def runCKProfiler(Map conf=[:]){
show_node_info() show_node_info()
...@@ -189,7 +214,6 @@ def runCKProfiler(Map conf=[:]){ ...@@ -189,7 +214,6 @@ def runCKProfiler(Map conf=[:]){
} }
def variant = env.STAGE_NAME def variant = env.STAGE_NAME
def retimage def retimage
gitStatusWrapper(credentialsId: "${status_wrapper_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCmSoftwarePlatform', repo: 'composable_kernel') { gitStatusWrapper(credentialsId: "${status_wrapper_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCmSoftwarePlatform', repo: 'composable_kernel') {
...@@ -197,7 +221,14 @@ def runCKProfiler(Map conf=[:]){ ...@@ -197,7 +221,14 @@ def runCKProfiler(Map conf=[:]){
retimage = docker.build("${image}", dockerArgs + '.') retimage = docker.build("${image}", dockerArgs + '.')
withDockerContainer(image: image, args: dockerOpts) { withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES'){ timeout(time: 5, unit: 'MINUTES'){
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo | tee clinfo.log'
if ( runShell('grep -n "Number of devices:.*. 0" clinfo.log') ){
echo "GPU not found"
throw e
}
else{
echo "GPU is OK"
}
} }
} }
} }
...@@ -209,89 +240,69 @@ def runCKProfiler(Map conf=[:]){ ...@@ -209,89 +240,69 @@ def runCKProfiler(Map conf=[:]){
retimage = docker.build("${image}", dockerArgs + " --no-cache .") retimage = docker.build("${image}", dockerArgs + " --no-cache .")
withDockerContainer(image: image, args: dockerOpts) { withDockerContainer(image: image, args: dockerOpts) {
timeout(time: 5, unit: 'MINUTES'){ timeout(time: 5, unit: 'MINUTES'){
sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo' sh 'PATH="/opt/rocm/opencl/bin:/opt/rocm/opencl/bin/x86_64:$PATH" clinfo | tee clinfo.log'
if ( runShell('grep -n "Number of devices:.*. 0" clinfo.log') ){
echo "GPU not found"
throw e
}
else{
echo "GPU is OK"
}
} }
} }
} }
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') { withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 5, unit: 'HOURS') timeout(time: 24, unit: 'HOURS')
{ {
cmake_build(conf) cmake_build(conf)
dir("script"){ dir("script"){
//run gemm performance tests if (params.RUN_FULL_QA){
def gemm_log = "perf_gemm_${gpu_arch}.log" def qa_log = "qa_${gpu_arch}.log"
sh "rm -f ${gemm_log}"
sh "echo Branch name: ${env.BRANCH_NAME} > ${gemm_log}"
sh "echo Node name: ${NODE_NAME} >> ${gemm_log}"
sh "echo GPU_arch name: ${gpu_arch} >> ${gemm_log}"
sh "rocminfo | grep 'Compute Unit:' >> ${gemm_log} "
sh "hipcc --version | grep -e 'HIP version' >> ${gemm_log}"
if (params.USE_9110){ if (params.USE_9110){
sh "echo Environment type: CI_9110 >> ${gemm_log}" sh "./run_full_performance_tests.sh 1 QA_9110 ${gpu_arch} ${env.BRANCH_NAME} ${NODE_NAME}"
} }
else{ else{
sh "echo Environment type: CI_release >> ${gemm_log}" sh "./run_full_performance_tests.sh 1 QA_release ${gpu_arch} ${env.BRANCH_NAME} ${NODE_NAME}"
} }
sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${gemm_log}" archiveArtifacts "perf_gemm_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 0 0 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_resnet50_N256_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 1 0 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_resnet50_N4_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 2 0 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_bathced_gemm_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 3 0 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_grouped_gemm_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 0 1 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_fwd_conv_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 1 1 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_bwd_conv_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 2 1 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_fusion_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 3 1 0 1 0 5 | tee -a ${gemm_log}" archiveArtifacts "perf_reduction_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 0 2 0 1 0 5 | tee -a ${gemm_log}" // stash perf files to master
sh "./profile_gemm.sh gemm 1 2 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_gemm_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 2 2 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_resnet50_N256_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 3 2 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_resnet50_N4_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 0 3 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_bathced_gemm_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 1 3 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_grouped_gemm_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 2 3 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_fwd_conv_${gpu_arch}.log"
sh "./profile_gemm.sh gemm 3 3 0 1 0 5 | tee -a ${gemm_log}" stash name: "perf_bwd_conv_${gpu_arch}.log"
//results will be parsed, stored, and analyzed within the python script stash name: "perf_fusion_${gpu_arch}.log"
//the script will return 0 if the performance criteria are met stash name: "perf_reduction_${gpu_arch}.log"
//or return 1 if the criteria are not met //we will process results on the master node
archiveArtifacts "${gemm_log}"
sh "python3 process_perf_data.py ${gemm_log} "
//run resnet50 test
def resnet256_log = "perf_resnet50_N256_${gpu_arch}.log"
sh "rm -f ${resnet256_log}"
sh "echo Branch name: ${env.BRANCH_NAME} > ${resnet256_log}"
sh "echo Node name: ${NODE_NAME} >> ${resnet256_log}"
sh "echo GPU_arch name: ${gpu_arch} >> ${resnet256_log}"
sh "rocminfo | grep 'Compute Unit:' >> ${resnet256_log} "
sh "hipcc --version | grep -e 'HIP version' >> ${resnet256_log}"
if (params.USE_9110){
sh "echo Environment type: CI_9110 >> ${resnet256_log}"
} }
else{ else{
sh "echo Environment type: CI_release >> ${resnet256_log}"
}
sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${resnet256_log}"
//first run tests with N=256
sh "./profile_resnet50.sh conv_fwd_bias_relu 1 1 1 1 0 2 0 1 256 | tee -a ${resnet256_log}"
archiveArtifacts "${resnet256_log}"
sh "python3 process_perf_data.py ${resnet256_log} "
//then run with N=4
def resnet4_log = "perf_resnet50_N4_${gpu_arch}.log"
sh "rm -f ${resnet4_log}"
sh "echo Branch name: ${env.BRANCH_NAME} > ${resnet4_log}"
sh "echo Node name: ${NODE_NAME} >> ${resnet4_log}"
sh "echo GPU_arch name: ${gpu_arch} >> ${resnet4_log}"
sh "rocminfo | grep 'Compute Unit:' >> ${resnet4_log} "
sh "hipcc --version | grep -e 'HIP version' >> ${resnet4_log}"
if (params.USE_9110){ if (params.USE_9110){
sh "echo Environment type: CI_9110 >> ${resnet4_log}" sh "./run_performance_tests.sh 0 CI_9110 ${gpu_arch} ${env.BRANCH_NAME} ${NODE_NAME}"
} }
else{ else{
sh "echo Environment type: CI_release >> ${resnet4_log}" sh "./run_performance_tests.sh 0 CI_release ${gpu_arch} ${env.BRANCH_NAME} ${NODE_NAME}"
} }
sh "/opt/rocm/bin/amdclang++ --version | grep -e 'InstalledDir' >> ${resnet4_log}" archiveArtifacts "perf_gemm_${gpu_arch}.log"
sh "./profile_resnet50.sh conv_fwd_bias_relu 1 1 1 1 0 2 0 1 4 | tee -a ${resnet4_log}" archiveArtifacts "perf_resnet50_N256_${gpu_arch}.log"
archiveArtifacts "${resnet4_log}" archiveArtifacts "perf_resnet50_N4_${gpu_arch}.log"
sh "python3 process_perf_data.py ${resnet4_log} " // stash perf files to master
stash name: "perf_gemm_${gpu_arch}.log"
stash name: "perf_resnet50_N256_${gpu_arch}.log"
stash name: "perf_resnet50_N4_${gpu_arch}.log"
//we will process the results on the master node
}
} }
} }
} }
...@@ -299,7 +310,6 @@ def runCKProfiler(Map conf=[:]){ ...@@ -299,7 +310,6 @@ def runCKProfiler(Map conf=[:]){
return retimage return retimage
} }
def runPerfTest(Map conf=[:]){ def runPerfTest(Map conf=[:]){
try{ try{
runCKProfiler(conf) runCKProfiler(conf)
...@@ -316,8 +326,76 @@ def runPerfTest(Map conf=[:]){ ...@@ -316,8 +326,76 @@ def runPerfTest(Map conf=[:]){
} }
} }
def process_results(Map conf=[:]){
env.HSA_ENABLE_SDMA=0
checkout scm
def image = "composable_kernels"
def prefixpath = "/opt/rocm"
def gpu_arch = conf.get("gpu_arch", "gfx908")
// Jenkins is complaining about the render group
def dockerOpts="--cap-add=SYS_PTRACE --security-opt seccomp=unconfined"
if (conf.get("enforce_xnack_on", false)) {
dockerOpts = dockerOpts + " --env HSA_XNACK=1"
}
def dockerArgs = "--build-arg PREFIX=${prefixpath} --build-arg GPU_ARCH='${gpu_arch}' --build-arg compiler_version='release' "
def variant = env.STAGE_NAME
def retimage
gitStatusWrapper(credentialsId: "${status_wrapper_creds}", gitHubContext: "Jenkins - ${variant}", account: 'ROCmSoftwarePlatform', repo: 'composable_kernel') {
try {
retimage = docker.build("${image}", dockerArgs + '.')
}
catch (org.jenkinsci.plugins.workflow.steps.FlowInterruptedException e){
echo "The job was cancelled or aborted"
throw e
}
}
withDockerContainer(image: image, args: dockerOpts + ' -v=/var/jenkins/:/var/jenkins') {
timeout(time: 1, unit: 'HOURS'){
try{
dir("script"){
if (params.RUN_FULL_QA){
// unstash perf files to master
unstash "perf_gemm_${gpu_arch}.log"
unstash "perf_resnet50_N256_${gpu_arch}.log"
unstash "perf_resnet50_N4_${gpu_arch}.log"
unstash "perf_bathced_gemm_${gpu_arch}.log"
unstash "perf_grouped_gemm_${gpu_arch}.log"
unstash "perf_fwd_conv_${gpu_arch}.log"
unstash "perf_bwd_conv_${gpu_arch}.log"
unstash "perf_fusion_${gpu_arch}.log"
unstash "perf_reduction_${gpu_arch}.log"
sh "./process_qa_data.sh ${gpu_arch}"
}
else{
// unstash perf files to master
unstash "perf_gemm_${gpu_arch}.log"
unstash "perf_resnet50_N256_${gpu_arch}.log"
unstash "perf_resnet50_N4_${gpu_arch}.log"
sh "./process_perf_data.sh ${gpu_arch}"
}
}
}
catch(e){
echo "throwing error exception while processing performance test results"
echo 'Exception occurred: ' + e.toString()
throw e
}
}
}
}
//launch develop branch daily at 23:00 in FULL_QA mode
CRON_SETTINGS = BRANCH_NAME == "develop" ? '''0 23 * * * % RUN_FULL_QA=true;USE_9110=true''' : ""
pipeline { pipeline {
agent none agent none
triggers {
cron(CRON_SETTINGS)
}
options { options {
parallelsAlwaysFailFast() parallelsAlwaysFailFast()
} }
...@@ -325,7 +403,11 @@ pipeline { ...@@ -325,7 +403,11 @@ pipeline {
booleanParam( booleanParam(
name: "USE_9110", name: "USE_9110",
defaultValue: true, defaultValue: true,
description: "") description: "Select compiler version: 9110 (default) or release")
booleanParam(
name: "RUN_FULL_QA",
defaultValue: false,
description: "Select whether to run small set of performance tests (default) or full QA")
} }
environment{ environment{
dbuser = "${dbuser}" dbuser = "${dbuser}"
...@@ -438,6 +520,25 @@ pipeline { ...@@ -438,6 +520,25 @@ pipeline {
} }
} }
} }
stage("Process Performance Test Results")
{
parallel
{
stage("Process results for gfx908"){
agent { label 'mici' }
steps{
process_results(gpu_arch: "gfx908")
}
}
stage("Process results for gfx90a"){
agent { label 'mici' }
steps{
process_results(gpu_arch: "gfx90a")
}
}
}
}
/* enable after the cmake file supports packaging /* enable after the cmake file supports packaging
stage("Packages") { stage("Packages") {
when { when {
......
example/15_grouped_gemm/grouped_gemm_xdl_fp16.cpp
View file @ 926008bc
...@@ -29,34 +29,39 @@ using Col = ck::tensor_layout::gemm::ColumnMajor; ...@@ -29,34 +29,39 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using ADataType = ck::half_t; using ADataType = F16;
using BDataType = ck::half_t; using BDataType = F16;
using CDataType = ck::half_t; using AccDataType = F32;
using AccDataType = float; using CShuffleDataType = F16;
using DsDataType = ck::Tuple<>;
using EDataType = F16;
using ALayout = ck::tensor_layout::gemm::RowMajor; using ALayout = Row;
using BLayout = ck::tensor_layout::gemm::ColumnMajor; using BLayout = Col;
using CLayout = ck::tensor_layout::gemm::RowMajor; using ELayout = Row;
using AElementOp = ck::tensor_operation::element_wise::PassThrough; using AElementOp = PassThrough;
using BElementOp = ck::tensor_operation::element_wise::PassThrough; using BElementOp = PassThrough;
using CElementOp = ck::tensor_operation::element_wise::PassThrough; using CDEElementOp = PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default; static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// static constexpr auto GemmMNPadding =
// ck::tensor_operation::device::GemmSpecialization::MNPadding;
// clang-format off
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemmXdl using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemmXdl
//######| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer| Num| // clang-format off
//######| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Spacialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar| Prefetch| //######| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//######| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector| | //######| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //######| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
< F16, F16, F16, F32, Row, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1, 1>; //######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
// clang-format on // clang-format on
using ReferenceGemmInstance = ck::tensor_operation::host:: using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
ReferenceGemm<ADataType, BDataType, CDataType, AccDataType, AElementOp, BElementOp, CElementOp>; BDataType,
EDataType,
AccDataType,
AElementOp,
BElementOp,
CDEElementOp>;
int main(int argc, char* argv[]) int main(int argc, char* argv[])
{ {
...@@ -81,11 +86,11 @@ int main(int argc, char* argv[]) ...@@ -81,11 +86,11 @@ int main(int argc, char* argv[])
int group_count = rand() % 16 + 1; int group_count = rand() % 16 + 1;
// GEMM shape // GEMM shape
std::vector<ck::tensor_operation::device::GemmShape> gemm_shapes; std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
std::vector<const void*> p_a, p_b; std::vector<const void*> p_a, p_b;
std::vector<void*> p_c; std::vector<void*> p_c;
gemm_shapes.reserve(group_count); gemm_descs.reserve(group_count);
for(int i = 0; i < group_count; i++) for(int i = 0; i < group_count; i++)
{ {
...@@ -93,7 +98,11 @@ int main(int argc, char* argv[]) ...@@ -93,7 +98,11 @@ int main(int argc, char* argv[])
int N = 128 + 128 * i; int N = 128 + 128 * i;
int K = 64 + 64 * i; int K = 64 + 64 * i;
gemm_shapes.push_back({M, N, K, K, K, N}); int stride_A = K;
int stride_B = K;
int stride_C = N;
gemm_descs.push_back({M, N, K, stride_A, stride_B, stride_C, {}});
} }
auto f_host_tensor_descriptor = auto f_host_tensor_descriptor =
...@@ -111,10 +120,9 @@ int main(int argc, char* argv[]) ...@@ -111,10 +120,9 @@ int main(int argc, char* argv[])
}; };
std::vector<Tensor<ADataType>> a_tensors; std::vector<Tensor<ADataType>> a_tensors;
;
std::vector<Tensor<BDataType>> b_tensors; std::vector<Tensor<BDataType>> b_tensors;
std::vector<Tensor<CDataType>> c_host_tensors; std::vector<Tensor<EDataType>> c_host_tensors;
std::vector<Tensor<CDataType>> c_device_tensors; std::vector<Tensor<EDataType>> c_device_tensors;
a_tensors.reserve(group_count); a_tensors.reserve(group_count);
b_tensors.reserve(group_count); b_tensors.reserve(group_count);
...@@ -131,25 +139,25 @@ int main(int argc, char* argv[]) ...@@ -131,25 +139,25 @@ int main(int argc, char* argv[])
std::size_t flop = 0, num_btype = 0; std::size_t flop = 0, num_btype = 0;
for(std::size_t i = 0; i < gemm_shapes.size(); i++) for(std::size_t i = 0; i < gemm_descs.size(); i++)
{ {
a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor( a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
gemm_shapes[i].M, gemm_shapes[i].K, gemm_shapes[i].StrideA, ALayout{}))); gemm_descs[i].M_, gemm_descs[i].K_, gemm_descs[i].stride_A_, ALayout{})));
b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor( b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
gemm_shapes[i].K, gemm_shapes[i].N, gemm_shapes[i].StrideB, BLayout{}))); gemm_descs[i].K_, gemm_descs[i].N_, gemm_descs[i].stride_B_, BLayout{})));
c_host_tensors.push_back(Tensor<CDataType>(f_host_tensor_descriptor( c_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
gemm_shapes[i].M, gemm_shapes[i].N, gemm_shapes[i].StrideC, CLayout{}))); gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
c_device_tensors.push_back(Tensor<CDataType>(f_host_tensor_descriptor( c_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
gemm_shapes[i].M, gemm_shapes[i].N, gemm_shapes[i].StrideC, CLayout{}))); gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
<< " b_k_n: " << b_tensors[i].mDesc << " c_m_n: " << c_device_tensors[i].mDesc << " b_k_n: " << b_tensors[i].mDesc << " c_m_n: " << c_device_tensors[i].mDesc
<< std::endl; << std::endl;
flop += std::size_t(2) * gemm_shapes[i].M * gemm_shapes[i].K * gemm_shapes[i].N; flop += std::size_t(2) * gemm_descs[i].M_ * gemm_descs[i].K_ * gemm_descs[i].N_;
num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() + num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() + sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
sizeof(CDataType) * c_device_tensors[i].mDesc.GetElementSize(); sizeof(EDataType) * c_device_tensors[i].mDesc.GetElementSize();
switch(init_method) switch(init_method)
{ {
...@@ -168,14 +176,14 @@ int main(int argc, char* argv[]) ...@@ -168,14 +176,14 @@ int main(int argc, char* argv[])
} }
} }
for(std::size_t i = 0; i < gemm_shapes.size(); i++) for(std::size_t i = 0; i < gemm_descs.size(); i++)
{ {
a_tensors_device.emplace_back( a_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpace())); std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpace()));
b_tensors_device.emplace_back( b_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(BDataType) * b_tensors[i].mDesc.GetElementSpace())); std::make_unique<DeviceMem>(sizeof(BDataType) * b_tensors[i].mDesc.GetElementSpace()));
c_tensors_device.emplace_back(std::make_unique<DeviceMem>( c_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(CDataType) * c_device_tensors[i].mDesc.GetElementSpace())); sizeof(EDataType) * c_device_tensors[i].mDesc.GetElementSpace()));
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data()); a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data()); b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
...@@ -187,14 +195,16 @@ int main(int argc, char* argv[]) ...@@ -187,14 +195,16 @@ int main(int argc, char* argv[])
auto a_element_op = AElementOp{}; auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{}; auto b_element_op = BElementOp{};
auto c_element_op = CElementOp{}; auto c_element_op = CDEElementOp{};
auto gemm = DeviceGemmInstance{}; auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker(); auto invoker = gemm.MakeInvoker();
std::vector<std::array<const void*, 0>> p_Ds = {};
// do GEMM // do GEMM
auto argument = auto argument = gemm.MakeArgument(
gemm.MakeArgument(p_a, p_b, p_c, gemm_shapes, a_element_op, b_element_op, c_element_op); p_a, p_b, p_Ds, p_c, gemm_descs, a_element_op, b_element_op, c_element_op);
DeviceMem gemm_desc_workspace(gemm.GetWorkSpaceSize(&argument)); DeviceMem gemm_desc_workspace(gemm.GetWorkSpaceSize(&argument));
...@@ -219,7 +229,7 @@ int main(int argc, char* argv[]) ...@@ -219,7 +229,7 @@ int main(int argc, char* argv[])
bool pass = true; bool pass = true;
if(do_verification) if(do_verification)
{ {
for(std::size_t i = 0; i < gemm_shapes.size(); i++) for(std::size_t i = 0; i < gemm_descs.size(); i++)
{ {
c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data()); c_tensors_device[i]->FromDevice(c_device_tensors[i].mData.data());
auto ref_gemm = ReferenceGemmInstance{}; auto ref_gemm = ReferenceGemmInstance{};
......
example/28_grouped_gemm_bias/CMakeLists.txt 0 → 100644
View file @ 926008bc
add_example_executable(example_grouped_gemm_bias_xdl_fp16 grouped_gemm_bias_xdl_fp16.cpp)
example/28_grouped_gemm_bias/grouped_gemm_bias_xdl_fp16.cpp 0 → 100644
View file @ 926008bc
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#include <iostream>
#include <numeric>
#include <initializer_list>
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/gemm_specialization.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm_xdl.hpp"
#include "ck/tensor_operation/gpu/element/binary_element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp"
#include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
template <ck::index_t... Is>
using S = ck::Sequence<Is...>;
using F16 = ck::half_t;
using F32 = float;
using Row = ck::tensor_layout::gemm::RowMajor;
using Col = ck::tensor_layout::gemm::ColumnMajor;
using PassThrough = ck::tensor_operation::element_wise::PassThrough;
using Add = ck::tensor_operation::element_wise::Add;
using ADataType = F16;
using BDataType = F16;
using AccDataType = F32;
using CShuffleDataType = F16;
using D0DataType = F16;
using DsDataType = ck::Tuple<D0DataType>;
using EDataType = F16;
using ALayout = Row;
using BLayout = Col;
using ELayout = Row;
using AElementOp = PassThrough;
using BElementOp = PassThrough;
using CDEElementOp = Add;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
using DeviceGemmInstance = ck::tensor_operation::device::DeviceGroupedGemmXdl
// clang-format off
//######| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| CDE| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//######| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//######| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//######| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
< ALayout, BLayout, ELayout, ADataType, BDataType, AccDataType, CShuffleDataType, DsDataType, EDataType, AElementOp, BElementOp, CDEElementOp, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>;
// clang-format on
int main(int argc, char* argv[])
{
bool do_verification = true;
int init_method = 1;
bool time_kernel = false;
if(argc == 4)
{
do_verification = std::stoi(argv[1]);
init_method = std::stoi(argv[2]);
time_kernel = std::stoi(argv[3]);
}
else
{
printf("arg1: verification (0=no, 1=yes)\n");
printf("arg2: initialization (0=no init, 1=integer value, 2=decimal value)\n");
printf("arg3: time kernel (0=n0, 1=yes)\n");
exit(0);
}
int group_count = rand() % 16 + 1;
// GEMM shape
std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
std::vector<const void*> p_a, p_b;
std::vector<std::array<const void*, 1>> p_ds;
std::vector<void*> p_c;
gemm_descs.reserve(group_count);
for(int i = 0; i < group_count; i++)
{
int M = 256 + 256 * i;
int N = 128 + 128 * i;
int K = 64 + 64 * i;
int stride_A = K;
int stride_B = K;
int stride_C = N;
std::vector<ck::index_t> stride_Ds = {0};
gemm_descs.push_back({M, N, K, stride_A, stride_B, stride_C, stride_Ds});
}
auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(std::is_same<decltype(layout), ck::tensor_layout::gemm::RowMajor>::value)
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({stride, 1}));
}
else
{
return HostTensorDescriptor(std::vector<std::size_t>({row, col}),
std::vector<std::size_t>({1, stride}));
}
};
std::vector<Tensor<ADataType>> a_tensors;
std::vector<Tensor<BDataType>> b_tensors;
std::vector<Tensor<D0DataType>> d0_tensors;
std::vector<Tensor<EDataType>> e_host_tensors;
std::vector<Tensor<EDataType>> e_device_tensors;
a_tensors.reserve(group_count);
b_tensors.reserve(group_count);
d0_tensors.reserve(group_count);
e_host_tensors.reserve(group_count);
e_device_tensors.reserve(group_count);
using DeviceMemPtr = std::unique_ptr<DeviceMem>;
std::vector<DeviceMemPtr> a_tensors_device, b_tensors_device, d0_tensors_device,
e_tensors_device;
a_tensors_device.reserve(group_count);
b_tensors_device.reserve(group_count);
d0_tensors_device.reserve(group_count);
e_tensors_device.reserve(group_count);
std::size_t flop = 0, num_btype = 0;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
a_tensors.push_back(Tensor<ADataType>(f_host_tensor_descriptor(
gemm_descs[i].M_, gemm_descs[i].K_, gemm_descs[i].stride_A_, ALayout{})));
b_tensors.push_back(Tensor<BDataType>(f_host_tensor_descriptor(
gemm_descs[i].K_, gemm_descs[i].N_, gemm_descs[i].stride_B_, BLayout{})));
d0_tensors.push_back(Tensor<D0DataType>(f_host_tensor_descriptor(
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_Ds_[0], ELayout{})));
e_host_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
e_device_tensors.push_back(Tensor<EDataType>(f_host_tensor_descriptor(
gemm_descs[i].M_, gemm_descs[i].N_, gemm_descs[i].stride_C_, ELayout{})));
std::cout << "gemm[" << i << "] a_m_k: " << a_tensors[i].mDesc
<< " b_k_n: " << b_tensors[i].mDesc << " c_m_n: " << e_device_tensors[i].mDesc
<< std::endl;
flop += std::size_t(2) * gemm_descs[i].M_ * gemm_descs[i].K_ * gemm_descs[i].N_;
num_btype += sizeof(ADataType) * a_tensors[i].mDesc.GetElementSize() +
sizeof(BDataType) * b_tensors[i].mDesc.GetElementSize() +
sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSize();
switch(init_method)
{
case 0: break;
case 1:
a_tensors[i].GenerateTensorValue(GeneratorTensor_2<ADataType>{-5, 5});
b_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
d0_tensors[i].GenerateTensorValue(GeneratorTensor_2<BDataType>{-5, 5});
break;
case 2:
a_tensors[i].GenerateTensorValue(GeneratorTensor_3<ADataType>{0.0, 1.0});
b_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
d0_tensors[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5});
break;
default:
a_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
b_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<1>{});
d0_tensors[i].GenerateTensorValue(GeneratorTensor_Sequential<0>{});
}
}
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
a_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(ADataType) * a_tensors[i].mDesc.GetElementSpace()));
b_tensors_device.emplace_back(
std::make_unique<DeviceMem>(sizeof(BDataType) * b_tensors[i].mDesc.GetElementSpace()));
d0_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(D0DataType) * d0_tensors[i].mDesc.GetElementSpace()));
e_tensors_device.emplace_back(std::make_unique<DeviceMem>(
sizeof(EDataType) * e_device_tensors[i].mDesc.GetElementSpace()));
a_tensors_device[i]->ToDevice(a_tensors[i].mData.data());
b_tensors_device[i]->ToDevice(b_tensors[i].mData.data());
d0_tensors_device[i]->ToDevice(d0_tensors[i].mData.data());
p_a.push_back(a_tensors_device[i]->GetDeviceBuffer());
p_b.push_back(b_tensors_device[i]->GetDeviceBuffer());
p_ds.push_back({d0_tensors_device[i]->GetDeviceBuffer()});
p_c.push_back(e_tensors_device[i]->GetDeviceBuffer());
}
auto a_element_op = AElementOp{};
auto b_element_op = BElementOp{};
auto cde_element_op = CDEElementOp{};
auto gemm = DeviceGemmInstance{};
auto invoker = gemm.MakeInvoker();
// do GEMM
auto argument = gemm.MakeArgument(
p_a, p_b, p_ds, p_c, gemm_descs, a_element_op, b_element_op, cde_element_op);
DeviceMem gemm_desc_workspace(gemm.GetWorkSpaceSize(&argument));
gemm.SetWorkSpacePointer(&argument, gemm_desc_workspace.GetDeviceBuffer());
if(!gemm.IsSupportedArgument(argument))
{
throw std::runtime_error(
"wrong! device_gemm with the specified compilation parameters does "
"not support this GEMM problem");
}
float ave_time = invoker.Run(argument, StreamConfig{nullptr, time_kernel});
float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
float gb_per_sec = num_btype / 1.E6 / ave_time;
std::cout << "Perf: " << ave_time << " ms, " << tflops << " TFlops, " << gb_per_sec << " GB/s, "
<< gemm.GetTypeString() << std::endl;
bool pass = true;
if(do_verification)
{
using ReferenceGemmInstance = ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType,
EDataType,
AccDataType,
AElementOp,
BElementOp,
PassThrough>;
for(std::size_t i = 0; i < gemm_descs.size(); i++)
{
e_tensors_device[i]->FromDevice(e_device_tensors[i].mData.data());
auto ref_gemm = ReferenceGemmInstance{};
auto ref_invoker = ref_gemm.MakeInvoker();
auto ref_argument = ref_gemm.MakeArgument(a_tensors[i],
b_tensors[i],
e_host_tensors[i],
a_element_op,
b_element_op,
PassThrough{});
ref_invoker.Run(ref_argument);
for(int m = 0; m < gemm_descs[i].M_; ++m)
{
for(int n = 0; n < gemm_descs[i].N_; ++n)
{
cde_element_op(
e_host_tensors[i](m, n), e_host_tensors[i](m, n), d0_tensors[i](m, n));
}
}
pass &= ck::utils::check_err(e_device_tensors[i].mData, e_host_tensors[i].mData);
}
}
return pass ? 0 : 1;
}
example/CMakeLists.txt
View file @ 926008bc
...@@ -46,4 +46,5 @@ add_subdirectory(24_batched_gemm_c_permute) ...@@ -46,4 +46,5 @@ add_subdirectory(24_batched_gemm_c_permute)
add_subdirectory(25_gemm_bias_c_permute) add_subdirectory(25_gemm_bias_c_permute)
add_subdirectory(26_contraction) add_subdirectory(26_contraction)
add_subdirectory(27_layernorm) add_subdirectory(27_layernorm)
add_subdirectory(28_grouped_gemm_bias)
add_subdirectory(29_batched_gemm_multi_d) add_subdirectory(29_batched_gemm_multi_d)
\ No newline at end of file
include/ck/tensor_operation/gpu/device/device_gemm.hpp
View file @ 926008bc
...@@ -12,12 +12,6 @@ namespace ck { ...@@ -12,12 +12,6 @@ namespace ck {
namespace tensor_operation { namespace tensor_operation {
namespace device { namespace device {
struct GemmShape
{
ck::index_t M, N, K;
ck::index_t StrideA, StrideB, StrideC;
};
template <typename ALayout, template <typename ALayout,
typename BLayout, typename BLayout,
typename CLayout, typename CLayout,
...@@ -65,29 +59,6 @@ using DeviceGemmPtr = std::unique_ptr<DeviceGemm<ALayout, ...@@ -65,29 +59,6 @@ using DeviceGemmPtr = std::unique_ptr<DeviceGemm<ALayout,
BElementwiseOperation, BElementwiseOperation,
CElementwiseOperation>>; CElementwiseOperation>>;
template <typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
struct DeviceGroupedGemm : public BaseOperator
{
virtual std::unique_ptr<BaseArgument> MakeArgumentPointer(std::vector<const void*>& p_a,
std::vector<const void*>& p_b,
std::vector<void*>& p_c,
std::vector<GemmShape>& gemm_shapes,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op,
ck::index_t KBatch = 1) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
template <typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
using DeviceGroupedGemmPtr = std::unique_ptr<
DeviceGroupedGemm<AElementwiseOperation, BElementwiseOperation, CElementwiseOperation>>;
} // namespace device } // namespace device
} // namespace tensor_operation } // namespace tensor_operation
} // namespace ck } // namespace ck
include/ck/tensor_operation/gpu/device/device_grouped_gemm.hpp 0 → 100644
View file @ 926008bc
#pragma once
#include <iostream>
#include <vector>
#include "device_base.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
struct GemmDesc
{
ck::index_t M_, N_, K_;
ck::index_t stride_A_, stride_B_, stride_C_;
std::vector<ck::index_t> stride_Ds_;
};
template <typename ALayout,
typename BLayout,
typename DELayout,
typename ADataType,
typename BDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
struct DeviceGroupedGemm : public BaseOperator
{
static constexpr index_t NumDTensor = DsDataType::Size();
virtual std::unique_ptr<BaseArgument>
MakeArgumentPointer(std::vector<const void*>& p_a,
std::vector<const void*>& p_b,
std::vector<std::array<const void*, NumDTensor>>& p_ds,
std::vector<void*>& p_e,
std::vector<GemmDesc>& gemm_desc,
AElementwiseOperation a_element_op,
BElementwiseOperation b_element_op,
CElementwiseOperation c_element_op) = 0;
virtual std::unique_ptr<BaseInvoker> MakeInvokerPointer() = 0;
};
template <typename ALayout,
typename BLayout,
typename DELayout,
typename ADataType,
typename BDataType,
typename DsDataType,
typename EDataType,
typename AElementwiseOperation,
typename BElementwiseOperation,
typename CElementwiseOperation>
using DeviceGroupedGemmPtr = std::unique_ptr<DeviceGroupedGemm<ALayout,
BLayout,
DELayout,
ADataType,
BDataType,
DsDataType,
EDataType,
AElementwiseOperation,
BElementwiseOperation,
CElementwiseOperation>>;
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/host_tensor/host_tensor.hpp
View file @ 926008bc
...@@ -381,52 +381,3 @@ HostTensorDescriptor::HostTensorDescriptor(const std::vector<X>& lens, ...@@ -381,52 +381,3 @@ HostTensorDescriptor::HostTensorDescriptor(const std::vector<X>& lens,
: mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end()) : mLens(lens.begin(), lens.end()), mStrides(strides.begin(), strides.end())
{ {
} }
#if 1
// FIXME: remove
template <typename T>
float check_error(const Tensor<T>& ref, const Tensor<T>& result)
{
float l1_error = 0;
float linf_error = -1;
float linf_rel_error = -1;
float linf_ref_value = 0, linf_result_value = 0;
float linf_rel_ref_value = 0, linf_rel_result_value = 0;
constexpr float eps = 1e-10;
for(std::size_t i = 0; i < ref.mData.size(); ++i)
{
float ref_v = ck::type_convert<float>(ref.mData[i]);
float result_v = ck::type_convert<float>(result.mData[i]);
float diff = std::abs(ref_v - result_v);
float rel_diff = diff / std::max(std::abs(ref_v), eps);
l1_error += diff;
if(linf_error < diff)
{
linf_error = diff;
linf_ref_value = ref_v;
linf_result_value = result_v;
}
if(linf_rel_error < rel_diff)
{
linf_rel_error = rel_diff;
linf_rel_ref_value = ref_v;
linf_rel_result_value = result_v;
}
}
std::cout << "Absolute Error L1 Norm (sum of abs diff): " << l1_error << std::endl;
std::cout << "Absolute Error L-inf Norm (max abs diff): " << linf_error << ", ref "
<< linf_ref_value << ", result " << linf_result_value << std::endl;
std::cout << "Relative Error L-inf Norm (max relative abs diff): " << linf_rel_error << ", ref "
<< linf_rel_ref_value << ", result " << linf_rel_result_value << std::endl;
return linf_error;
}
#endif
library/include/ck/library/tensor_operation_instance/gpu/grouped_gemm.hpp 0 → 100644
View file @ 926008bc
// SPDX-License-Identifier: MIT
// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
#pragma once
#include <cstdlib>
#include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
#include "ck/library/tensor_operation_instance/device_operation_instance_factory.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using DsType = Tuple<>;
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
Row,
Row,
F16,
F16,
DsType,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Row,
Col,
Row,
F16,
F16,
DsType,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Col,
Row,
Row,
F16,
F16,
DsType,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(
std::vector<std::unique_ptr<DeviceGroupedGemm<Col,
Col,
Row,
F16,
F16,
DsType,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances);
template <typename ALayout,
typename BLayout,
typename CLayout,
typename ADataType,
typename BDataType,
typename DsDataType,
typename EDataType>
struct DeviceOperationInstanceFactory<ck::tensor_operation::device::DeviceGroupedGemm<
ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
DsDataType,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>>
{
using DeviceOp = DeviceGroupedGemm<ALayout,
BLayout,
CLayout,
ADataType,
BDataType,
DsDataType,
EDataType,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
static auto GetInstances()
{
std::vector<std::unique_ptr<DeviceOp>> op_ptrs;
if constexpr(is_same_v<ADataType, half_t> && is_same_v<BDataType, half_t> &&
is_same_v<EDataType, half_t>)
{
if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>)
{
add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(op_ptrs);
}
else if constexpr(is_same_v<ALayout, Row> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Row> &&
is_same_v<CLayout, Row>)
{
add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(op_ptrs);
}
else if constexpr(is_same_v<ALayout, Col> && is_same_v<BLayout, Col> &&
is_same_v<CLayout, Row>)
{
add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(op_ptrs);
}
}
return op_ptrs;
}
};
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
library/include/ck/library/utility/check_err.hpp
View file @ 926008bc
...@@ -29,9 +29,8 @@ check_err(const std::vector<T>& out, ...@@ -29,9 +29,8 @@ check_err(const std::vector<T>& out,
{ {
if(out.size() != ref.size()) if(out.size() != ref.size())
{ {
std::cout << "out.size() != ref.size(), :" << out.size() << " != " << ref.size() std::cout << msg << " out.size() != ref.size(), :" << out.size() << " != " << ref.size()
<< std::endl << std::endl;
<< msg << std::endl;
return false; return false;
} }
...@@ -48,9 +47,8 @@ check_err(const std::vector<T>& out, ...@@ -48,9 +47,8 @@ check_err(const std::vector<T>& out,
err_count++; err_count++;
if(err_count < 5) if(err_count < 5)
{ {
std::cout << std::setw(12) << std::setprecision(7) << "out[" << i << "] != ref[" std::cout << msg << std::setw(12) << std::setprecision(7) << " out[" << i
<< i << "]: " << out[i] << " != " << ref[i] << std::endl << "] != ref[" << i << "]: " << out[i] << " != " << ref[i] << std::endl;
<< msg << std::endl;
} }
res = false; res = false;
} }
...@@ -72,9 +70,8 @@ check_err(const std::vector<T>& out, ...@@ -72,9 +70,8 @@ check_err(const std::vector<T>& out,
{ {
if(out.size() != ref.size()) if(out.size() != ref.size())
{ {
std::cout << "out.size() != ref.size(), :" << out.size() << " != " << ref.size() std::cout << msg << " out.size() != ref.size(), :" << out.size() << " != " << ref.size()
<< std::endl << std::endl;
<< msg << std::endl;
return false; return false;
} }
...@@ -94,9 +91,8 @@ check_err(const std::vector<T>& out, ...@@ -94,9 +91,8 @@ check_err(const std::vector<T>& out,
err_count++; err_count++;
if(err_count < 5) if(err_count < 5)
{ {
std::cout << std::setw(12) << std::setprecision(7) << "out[" << i << "] != ref[" std::cout << msg << std::setw(12) << std::setprecision(7) << " out[" << i
<< i << "]: " << o << " != " << r << std::endl << "] != ref[" << i << "]: " << o << " != " << r << std::endl;
<< msg << std::endl;
} }
res = false; res = false;
} }
...@@ -118,9 +114,8 @@ check_err(const std::vector<T>& out, ...@@ -118,9 +114,8 @@ check_err(const std::vector<T>& out,
{ {
if(out.size() != ref.size()) if(out.size() != ref.size())
{ {
std::cout << "out.size() != ref.size(), :" << out.size() << " != " << ref.size() std::cout << msg << " out.size() != ref.size(), :" << out.size() << " != " << ref.size()
<< std::endl << std::endl;
<< msg << std::endl;
return false; return false;
} }
...@@ -139,9 +134,8 @@ check_err(const std::vector<T>& out, ...@@ -139,9 +134,8 @@ check_err(const std::vector<T>& out,
err_count++; err_count++;
if(err_count < 5) if(err_count < 5)
{ {
std::cout << std::setw(12) << std::setprecision(7) << "out[" << i << "] != ref[" std::cout << msg << std::setw(12) << std::setprecision(7) << " out[" << i
<< i << "]: " << o << " != " << r << std::endl << "] != ref[" << i << "]: " << o << " != " << r << std::endl;
<< msg << std::endl;
} }
res = false; res = false;
} }
...@@ -163,9 +157,8 @@ check_err(const std::vector<T>& out, ...@@ -163,9 +157,8 @@ check_err(const std::vector<T>& out,
{ {
if(out.size() != ref.size()) if(out.size() != ref.size())
{ {
std::cout << "out.size() != ref.size(), :" << out.size() << " != " << ref.size() std::cout << msg << " out.size() != ref.size(), :" << out.size() << " != " << ref.size()
<< std::endl << std::endl;
<< msg << std::endl;
return false; return false;
} }
...@@ -185,9 +178,9 @@ check_err(const std::vector<T>& out, ...@@ -185,9 +178,9 @@ check_err(const std::vector<T>& out,
err_count++; err_count++;
if(err_count < 5) if(err_count < 5)
{ {
std::cout << "out[" << i << "] != ref[" << i << "]: " << static_cast<int>(out[i]) std::cout << msg << " out[" << i << "] != ref[" << i
<< " != " << static_cast<int>(ref[i]) << std::endl << "]: " << static_cast<int>(out[i]) << " != " << static_cast<int>(ref[i])
<< msg << std::endl; << std::endl;
} }
res = false; res = false;
} }
......
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instance.cpp
View file @ 926008bc
...@@ -23,6 +23,8 @@ using Col = ck::tensor_layout::gemm::ColumnMajor; ...@@ -23,6 +23,8 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
using DsType = ck::Tuple<>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default; static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
...@@ -30,23 +32,40 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa ...@@ -30,23 +32,40 @@ static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecializa
// Compilation parameters for a[k, m] * b[k, n] = c[m, n] // Compilation parameters for a[k, m] * b[k, n] = c[m, n]
using device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances = std::tuple< using device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances = std::tuple<
// clang-format off // clang-format off
//#################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer| //##################| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//#################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar| //##################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//#################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector| //##################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//#################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //##################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 256, 128, 32, 2, 2, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 2, 2, 32, 32, 2, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 2, 2, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 2, 2, 32, 32, 2, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Row, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, 7, 1> DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 2, 2, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 2, 2, 32, 32, 2, 2, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 2, 2, 32, 32, 2, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 2, 2, 32, 32, 1, 2, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Row, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on // clang-format on
>; >;
void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances( void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(
std::vector<DeviceGroupedGemmPtr<PassThrough, PassThrough, PassThrough>>& instances) std::vector<std::unique_ptr<DeviceGroupedGemm<Col,
Row,
Row,
F16,
F16,
DsType,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances{}); device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances{});
......
library/src/tensor_operation_instance/gpu/grouped_gemm/device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instance.cpp
View file @ 926008bc
...@@ -23,30 +23,48 @@ using Col = ck::tensor_layout::gemm::ColumnMajor; ...@@ -23,30 +23,48 @@ using Col = ck::tensor_layout::gemm::ColumnMajor;
template <ck::index_t... Is> template <ck::index_t... Is>
using S = ck::Sequence<Is...>; using S = ck::Sequence<Is...>;
using DsType = ck::Tuple<>;
using PassThrough = ck::tensor_operation::element_wise::PassThrough; using PassThrough = ck::tensor_operation::element_wise::PassThrough;
static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default; static constexpr auto GemmDefault = ck::tensor_operation::device::GemmSpecialization::Default;
// Compilation parameters for a[k, m] * b[n, k] = c[m, n]
using device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances = std::tuple< using device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances = std::tuple<
// clang-format off // clang-format off
//#################| AData| BData| CData| AccData| ALayout| BLayout| CLayout| A| B| C| GEMM| Block| MPer| NPer| K0Per| K1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CThreadTransfer| CThreadTransfer| //##################| ALayout| BLayout| ELayout| AData| BData| AccData| CShuffle| DsData| EData| A| B| C| GEMM| NumGemmK| Block| MPer| NPer| KPer| AK1| BK1| MPer| NPer| MXdl| NXdl| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockTransfer| ABlockLds| BBlockTransfer| BBlockTransfer| BBlockTransfer| BlockTransfer| BBlockTransfer| BBlockTransfer| BBlockLds| CShuffle| CShuffle| CBlockTransferClusterLengths| CBlockTransfer|
//#################| Type| Type| Type| Type| | | | Elementwise| Elementwise| Elementwise|Specialization| Size| Block| Block| Block| | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| SrcDstVectorDim| DstScalar| //##################| | | | Type| Type| Type| DataType| Type| Type| Elementwise| Elementwise| Elementwise| Spacialization| Prefetch| Size| Block| Block| Block| | | XDL| XDL| Per| Per| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraM| ThreadCluster| ThreadCluster| SrcAccessOrder| SrcVectorDim| SrcScalar| DstScalar| AddExtraN| MXdlPerWave| NXdlPerWave| _MBlock_MWaveMPerXdl| ScalarPerVector|
//#################| | | | | | | | Operation| Operation| Operation| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | | PerVector| //##################| | | | | | | | | | Operation| Operation| Operation| | Stage| | | | | | | | | Wave| Wave| Lengths_K0_M_K1| ArrangeOrder| | | PerVector| PerVector_K1| | Lengths_K0_N_K1| ArrangeOrder| | | PerVector| PerVector_K1| | PerShuffle| PerShuffle| _NBlock_NWaveNPerXdl| _NWaveNPerXdl|
//#################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | //##################| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 256, 128, 4, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 256, 128, 32, 2, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 256, 4, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 256, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 128, 4, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 2, 8, 32, 32, 2, 4, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 128, 4, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 256, 32, 8, 8, 32, 32, 2, 4, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 128, 64, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 2, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 128, 64, 128, 4, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 128, 32, 8, 8, 32, 32, 4, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 128, 64, 4, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1>, DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 2, 8, 32, 32, 2, 2, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< F16, F16, F16, F32, Col, Col, Row, PassThrough, PassThrough, PassThrough, GemmDefault, 256, 64, 128, 4, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, true, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, true, 7, 1> DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 2, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 128, 64, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 4>, 8>,
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 2, 8, 32, 32, 2, 2, S<8, 16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 128, 64, 128, 32, 8, 8, 32, 32, 2, 2, S<4, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 32, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 16, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 2, 8, 32, 32, 2, 1, S<8, 32, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 128, 64, 32, 8, 8, 32, 32, 2, 1, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 2, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 2, 8, 32, 32, 1, 2, S<16,16, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 4, 2, 0, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>,
DeviceGroupedGemmXdl< Col, Col, Row, F16, F16, F32, F16, DsType, F16, PassThrough, PassThrough, PassThrough, GemmDefault, 1, 256, 64, 128, 32, 8, 8, 32, 32, 1, 2, S<4, 64, 1>, S<0, 2, 1>, S<0, 2, 1>, 1, 1, 8, 1, S<4, 64, 1>, S<1, 0, 2>, S<1, 0, 2>, 2, 8, 8, 1, 1, 1, S<1, 32, 1, 8>, 8>
// clang-format on // clang-format on
>; >;
void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances( void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(
std::vector<DeviceGroupedGemmPtr<PassThrough, PassThrough, PassThrough>>& instances) std::vector<std::unique_ptr<DeviceGroupedGemm<Col,
Col,
Row,
F16,
F16,
DsType,
F16,
PassThrough,
PassThrough,
PassThrough>>>& instances)
{ {
add_device_operation_instances(instances, add_device_operation_instances(instances,
device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances{}); device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances{});
......
profiler/include/profile_batched_gemm_reduce_impl.hpp
View file @ 926008bc
...@@ -318,13 +318,16 @@ bool profile_batched_gemm_reduce_impl(int do_verification, ...@@ -318,13 +318,16 @@ bool profile_batched_gemm_reduce_impl(int do_verification,
reduce0_device_buf.FromDevice(d0_g_m_device_result.mData.data()); reduce0_device_buf.FromDevice(d0_g_m_device_result.mData.data());
reduce1_device_buf.FromDevice(d1_g_m_device_result.mData.data()); reduce1_device_buf.FromDevice(d1_g_m_device_result.mData.data());
float c_error = check_error(c_g_m_n_host_result, c_g_m_n_device_result); bool c_error =
float d0_error = check_error(d0_g_m_host_result, d0_g_m_device_result); ck::utils::check_err(c_g_m_n_host_result.mData, c_g_m_n_device_result.mData);
float d1_error = check_error(d1_g_m_host_result, d1_g_m_device_result); bool d0_error =
ck::utils::check_err(d0_g_m_host_result.mData, d0_g_m_device_result.mData);
pass = pass && (c_error < 1E-6); bool d1_error =
pass = pass && (d0_error < 1E-6); ck::utils::check_err(d1_g_m_host_result.mData, d1_g_m_device_result.mData);
pass = pass && (d1_error < 1E-6);
pass = pass && (c_error == true);
pass = pass && (d0_error == true);
pass = pass && (d1_error == true);
if(do_log) if(do_log)
{ {
......
profiler/include/profile_conv_bwd_weight_impl.hpp
View file @ 926008bc
...@@ -250,11 +250,11 @@ bool profile_conv_bwd_weight_impl(int do_verification, ...@@ -250,11 +250,11 @@ bool profile_conv_bwd_weight_impl(int do_verification,
{ {
wei_device_buf.FromDevice(wei_k_c_y_x_device_result.mData.data()); wei_device_buf.FromDevice(wei_k_c_y_x_device_result.mData.data());
float max_error = check_error(wei_k_c_y_x_host_result, wei_k_c_y_x_device_result); pass = ck::utils::check_err(wei_k_c_y_x_host_result.mData,
wei_k_c_y_x_device_result.mData);
if(max_error > 8) if(pass == false)
{ {
pass = false;
std::cout << "Fail info:" << conv_ptr->GetTypeString() << std::endl; std::cout << "Fail info:" << conv_ptr->GetTypeString() << std::endl;
} }
......
profiler/include/profile_convnd_bwd_data_impl.hpp
View file @ 926008bc
...@@ -8,6 +8,7 @@ ...@@ -8,6 +8,7 @@
#include "ck/tensor_operation/gpu/device/device_conv_bwd_data.hpp" #include "ck/tensor_operation/gpu/device/device_conv_bwd_data.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/conv_util.hpp" #include "ck/library/utility/conv_util.hpp"
#include "ck/library/host_tensor/device_memory.hpp" #include "ck/library/host_tensor/device_memory.hpp"
#include "ck/library/host_tensor/host_tensor.hpp" #include "ck/library/host_tensor/host_tensor.hpp"
...@@ -452,7 +453,7 @@ bool profile_convnd_bwd_data_impl(int do_verification, ...@@ -452,7 +453,7 @@ bool profile_convnd_bwd_data_impl(int do_verification,
std::cout << "Pass Info: " << conv_ptr->GetTypeString() << std::endl; std::cout << "Pass Info: " << conv_ptr->GetTypeString() << std::endl;
} }
check_error(input_host_result, input_device_result); success = ck::utils::check_err(input_host_result.mData, input_device_result.mData);
if(do_log) if(do_log)
{ {
......
profiler/include/profile_convnd_bwd_weight_impl.hpp
View file @ 926008bc
...@@ -433,21 +433,17 @@ bool profile_convnd_bwd_weight_impl(int do_verification, ...@@ -433,21 +433,17 @@ bool profile_convnd_bwd_weight_impl(int do_verification,
{ {
wei_device_buf.FromDevice(weights_device_result.mData.data()); wei_device_buf.FromDevice(weights_device_result.mData.data());
float max_error = check_error(weights_host_result, weights_device_result); success = ck::utils::check_err(weights_host_result.mData, weights_device_result.mData);
if(max_error > 8) if(success == false)
{ {
std::cout << "Fail Info: " << conv_ptr->GetTypeString() << std::endl; std::cout << "Fail Info: " << conv_ptr->GetTypeString() << std::endl;
success = false;
} }
else else
{ {
std::cout << "Pass Info: " << conv_ptr->GetTypeString() << std::endl; std::cout << "Pass Info: " << conv_ptr->GetTypeString() << std::endl;
} }
check_error(weights_host_result, weights_device_result);
if(do_log) if(do_log)
{ {
std::cout << "in : "; std::cout << "in : ";
......
profiler/include/profile_grouped_gemm_impl.hpp
View file @ 926008bc
...@@ -7,9 +7,11 @@ ...@@ -7,9 +7,11 @@
#include "ck/ck.hpp" #include "ck/ck.hpp"
#include "ck/tensor_operation/gpu/device/tensor_layout.hpp" #include "ck/tensor_operation/gpu/device/tensor_layout.hpp"
#include "ck/tensor_operation/gpu/device/device_gemm.hpp" #include "ck/tensor_operation/gpu/device/device_grouped_gemm.hpp"
#include "ck/tensor_operation/gpu/element/element_wise_operation.hpp" #include "ck/tensor_operation/gpu/element/element_wise_operation.hpp"
#include "ck/library/tensor_operation_instance/gpu/grouped_gemm.hpp"
#include "ck/library/utility/check_err.hpp" #include "ck/library/utility/check_err.hpp"
#include "ck/library/utility/conv_util.hpp" #include "ck/library/utility/conv_util.hpp"
#include "ck/library/host_tensor/device_memory.hpp" #include "ck/library/host_tensor/device_memory.hpp"
...@@ -17,41 +19,17 @@ ...@@ -17,41 +19,17 @@
#include "ck/library/host_tensor/host_tensor_generator.hpp" #include "ck/library/host_tensor/host_tensor_generator.hpp"
#include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp" #include "ck/library/reference_tensor_operation/cpu/reference_gemm.hpp"
namespace ck {
namespace tensor_operation {
namespace device {
namespace instance {
using DeviceGroupedGemmNoOpPtr = ck::tensor_operation::device::DeviceGroupedGemmPtr<
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough,
ck::tensor_operation::element_wise::PassThrough>;
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(
std::vector<DeviceGroupedGemmNoOpPtr>&);
void add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(
std::vector<DeviceGroupedGemmNoOpPtr>&);
void add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(
std::vector<DeviceGroupedGemmNoOpPtr>&);
void add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(
std::vector<DeviceGroupedGemmNoOpPtr>&);
} // namespace instance
} // namespace device
} // namespace tensor_operation
} // namespace ck
namespace ck { namespace ck {
namespace profiler { namespace profiler {
template <typename ADataType, template <typename ADataType,
typename BDataType, typename BDataType,
typename CDataType, typename EDataType,
typename AccDataType, typename AccDataType,
typename ALayout, typename ALayout,
typename BLayout, typename BLayout,
typename CLayout> typename CLayout>
void profile_grouped_gemm_impl(int do_verification, bool profile_grouped_gemm_impl(int do_verification,
int init_method, int init_method,
bool do_log, bool do_log,
bool time_kernel, bool time_kernel,
...@@ -62,6 +40,9 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -62,6 +40,9 @@ void profile_grouped_gemm_impl(int do_verification,
const std::vector<int>& StrideBs, const std::vector<int>& StrideBs,
const std::vector<int>& StrideCs) const std::vector<int>& StrideCs)
{ {
bool pass = true;
auto f_host_tensor_descriptor = auto f_host_tensor_descriptor =
[](std::size_t row, std::size_t col, std::size_t stride, auto layout) { [](std::size_t row, std::size_t col, std::size_t stride, auto layout) {
if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value) if(is_same<decltype(layout), tensor_layout::gemm::RowMajor>::value)
...@@ -86,7 +67,7 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -86,7 +67,7 @@ void profile_grouped_gemm_impl(int do_verification,
std::vector<Tensor<ADataType>> a_m_k; std::vector<Tensor<ADataType>> a_m_k;
std::vector<Tensor<BDataType>> b_k_n; std::vector<Tensor<BDataType>> b_k_n;
std::vector<Tensor<CDataType>> c_m_n_device_results; std::vector<Tensor<EDataType>> c_m_n_device_results;
for(std::size_t i = 0; i < group_count; i++) for(std::size_t i = 0; i < group_count; i++)
{ {
...@@ -96,7 +77,7 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -96,7 +77,7 @@ void profile_grouped_gemm_impl(int do_verification,
Tensor<BDataType>(f_host_tensor_descriptor(Ks[i], Ns[i], StrideBs[i], BLayout{}))); Tensor<BDataType>(f_host_tensor_descriptor(Ks[i], Ns[i], StrideBs[i], BLayout{})));
c_m_n_device_results.push_back( c_m_n_device_results.push_back(
Tensor<CDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{}))); Tensor<EDataType>(f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})));
std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_m_k[i].mDesc << ", b_k_n[" << i std::cout << "group: " << i << " a_m_k[" << i << "]:" << a_m_k[i].mDesc << ", b_k_n[" << i
<< "]:" << b_k_n[i].mDesc << ", c_m_n_device_results[" << i << "]:" << b_k_n[i].mDesc << ", c_m_n_device_results[" << i
...@@ -115,7 +96,7 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -115,7 +96,7 @@ void profile_grouped_gemm_impl(int do_verification,
b_k_n[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread); b_k_n[i].GenerateTensorValue(GeneratorTensor_3<BDataType>{-0.5, 0.5}, num_thread);
} }
c_m_n_device_results[i].GenerateTensorValue(GeneratorTensor_0<CDataType>{}, num_thread); c_m_n_device_results[i].GenerateTensorValue(GeneratorTensor_0<EDataType>{}, num_thread);
} }
using AElementOp = ck::tensor_operation::element_wise::PassThrough; using AElementOp = ck::tensor_operation::element_wise::PassThrough;
...@@ -145,9 +126,9 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -145,9 +126,9 @@ void profile_grouped_gemm_impl(int do_verification,
p_b.reserve(group_count); p_b.reserve(group_count);
p_c.reserve(group_count); p_c.reserve(group_count);
std::vector<ck::tensor_operation::device::GemmShape> gemm_shapes; std::vector<ck::tensor_operation::device::GemmDesc> gemm_descs;
gemm_shapes.reserve(group_count); gemm_descs.reserve(group_count);
for(std::size_t i = 0; i < group_count; i++) for(std::size_t i = 0; i < group_count; i++)
{ {
...@@ -157,56 +138,34 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -157,56 +138,34 @@ void profile_grouped_gemm_impl(int do_verification,
std::make_unique<DeviceMem>(sizeof(BDataType) * b_k_n[i].mDesc.GetElementSpace())); std::make_unique<DeviceMem>(sizeof(BDataType) * b_k_n[i].mDesc.GetElementSpace()));
c_device_buf.emplace_back(std::make_unique<DeviceMem>( c_device_buf.emplace_back(std::make_unique<DeviceMem>(
sizeof(CDataType) * c_m_n_device_results[i].mDesc.GetElementSpace())); sizeof(EDataType) * c_m_n_device_results[i].mDesc.GetElementSpace()));
a_device_buf[i]->ToDevice(a_m_k[i].mData.data()); a_device_buf[i]->ToDevice(a_m_k[i].mData.data());
b_device_buf[i]->ToDevice(b_k_n[i].mData.data()); b_device_buf[i]->ToDevice(b_k_n[i].mData.data());
c_device_buf[i]->ToDevice(c_m_n_device_results[i].mData.data()); c_device_buf[i]->ToDevice(c_m_n_device_results[i].mData.data());
gemm_shapes.push_back({Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i]}); gemm_descs.push_back({Ms[i], Ns[i], Ks[i], StrideAs[i], StrideBs[i], StrideCs[i], {}});
p_a.push_back(a_device_buf[i]->GetDeviceBuffer()); p_a.push_back(a_device_buf[i]->GetDeviceBuffer());
p_b.push_back(b_device_buf[i]->GetDeviceBuffer()); p_b.push_back(b_device_buf[i]->GetDeviceBuffer());
p_c.push_back(c_device_buf[i]->GetDeviceBuffer()); p_c.push_back(c_device_buf[i]->GetDeviceBuffer());
} }
// add device GEMM instances using DeviceOp = ck::tensor_operation::device::DeviceGroupedGemm<ALayout,
std::vector<ck::tensor_operation::device::instance::DeviceGroupedGemmNoOpPtr> gemm_ptrs; BLayout,
CLayout,
ADataType,
BDataType,
ck::Tuple<>,
EDataType,
AElementOp,
BElementOp,
CElementOp>;
if constexpr(is_same<ADataType, half_t>::value && is_same<BDataType, half_t>::value && const auto op_ptrs = ck::tensor_operation::device::instance::DeviceOperationInstanceFactory<
is_same<CDataType, half_t>::value) DeviceOp>::GetInstances();
{
if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
{
ck::tensor_operation::device::instance::
add_device_grouped_gemm_xdl_f16_f16_f16_mk_kn_mn_instances(gemm_ptrs);
}
else if constexpr(is_same<ALayout, tensor_layout::gemm::RowMajor>::value &&
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
{
ck::tensor_operation::device::instance::
add_device_grouped_gemm_xdl_f16_f16_f16_mk_nk_mn_instances(gemm_ptrs);
}
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
is_same<BLayout, tensor_layout::gemm::RowMajor>::value &&
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
{
ck::tensor_operation::device::instance::
add_device_grouped_gemm_xdl_f16_f16_f16_km_kn_mn_instances(gemm_ptrs);
}
else if constexpr(is_same<ALayout, tensor_layout::gemm::ColumnMajor>::value &&
is_same<BLayout, tensor_layout::gemm::ColumnMajor>::value &&
is_same<CLayout, tensor_layout::gemm::RowMajor>::value)
{
ck::tensor_operation::device::instance::
add_device_grouped_gemm_xdl_f16_f16_f16_km_nk_mn_instances(gemm_ptrs);
}
}
if(gemm_ptrs.size() <= 0) if(op_ptrs.size() <= 0)
{ {
throw std::runtime_error("wrong! no device GEMM instance found"); throw std::runtime_error("wrong! no device GEMM instance found");
} }
...@@ -216,14 +175,17 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -216,14 +175,17 @@ void profile_grouped_gemm_impl(int do_verification,
float best_tflops = 0; float best_tflops = 0;
float best_gb_per_sec = 0; float best_gb_per_sec = 0;
auto p_ds = std::vector<std::array<const void*, 0>>{};
// profile device GEMM instances // profile device GEMM instances
for(auto& gemm_ptr : gemm_ptrs) for(auto& gemm_ptr : op_ptrs)
{ {
auto argument_ptr = auto argument_ptr =
gemm_ptr->MakeArgumentPointer(p_a, gemm_ptr->MakeArgumentPointer(p_a,
p_b, p_b,
p_ds,
p_c, p_c,
gemm_shapes, gemm_descs,
ck::tensor_operation::element_wise::PassThrough{}, ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{}, ck::tensor_operation::element_wise::PassThrough{},
ck::tensor_operation::element_wise::PassThrough{}); ck::tensor_operation::element_wise::PassThrough{});
...@@ -242,12 +204,12 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -242,12 +204,12 @@ void profile_grouped_gemm_impl(int do_verification,
invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel}); invoker_ptr->Run(argument_ptr.get(), StreamConfig{nullptr, time_kernel});
std::size_t flop = 0, num_btype = 0; std::size_t flop = 0, num_btype = 0;
for(std::size_t i = 0; i < gemm_shapes.size(); i++) for(std::size_t i = 0; i < gemm_descs.size(); i++)
{ {
flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i]; flop += std::size_t(2) * Ms[i] * Ns[i] * Ks[i];
num_btype += sizeof(ADataType) * Ms[i] * Ks[i] + sizeof(BDataType) * Ks[i] * Ns[i] + num_btype += sizeof(ADataType) * Ms[i] * Ks[i] + sizeof(BDataType) * Ks[i] * Ns[i] +
sizeof(CDataType) * Ms[i] * Ns[i]; sizeof(EDataType) * Ms[i] * Ns[i];
} }
float tflops = static_cast<float>(flop) / 1.E9 / ave_time; float tflops = static_cast<float>(flop) / 1.E9 / ave_time;
...@@ -266,18 +228,18 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -266,18 +228,18 @@ void profile_grouped_gemm_impl(int do_verification,
if(do_verification) if(do_verification)
{ {
for(std::size_t i = 0; i < gemm_shapes.size(); i++) for(std::size_t i = 0; i < gemm_descs.size(); i++)
{ {
c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data()); c_device_buf[i]->FromDevice(c_m_n_device_results[i].mData.data());
Tensor<CDataType> c_m_n_host_result( Tensor<EDataType> c_m_n_host_result(
f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{})); f_host_tensor_descriptor(Ms[i], Ns[i], StrideCs[i], CLayout{}));
using ReferenceGemmInstance = using ReferenceGemmInstance =
ck::tensor_operation::host::ReferenceGemm<ADataType, ck::tensor_operation::host::ReferenceGemm<ADataType,
BDataType, BDataType,
CDataType, EDataType,
AccDataType, AccDataType,
AElementOp, AElementOp,
BElementOp, BElementOp,
...@@ -294,7 +256,8 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -294,7 +256,8 @@ void profile_grouped_gemm_impl(int do_verification,
c_element_op); c_element_op);
ref_invoker.Run(ref_argument); ref_invoker.Run(ref_argument);
ck::utils::check_err(c_m_n_device_results[i].mData, c_m_n_host_result.mData); pass = pass && ck::utils::check_err(c_m_n_device_results[i].mData,
c_m_n_host_result.mData);
if(do_log) if(do_log)
{ {
...@@ -319,6 +282,8 @@ void profile_grouped_gemm_impl(int do_verification, ...@@ -319,6 +282,8 @@ void profile_grouped_gemm_impl(int do_verification,
std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, " std::cout << "Best Perf: " << best_ave_time << " ms, " << best_tflops << " TFlops, "
<< best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl; << best_gb_per_sec << " GB/s, " << best_gemm_name << std::endl;
return pass;
} // namespace profiler } // namespace profiler
} // namespace profiler } // namespace profiler
......
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